Overview

Dataset statistics

Number of variables51
Number of observations41188
Missing cells0
Missing cells (%)0.0%
Duplicate rows1528
Duplicate rows (%)3.7%
Total size in memory3.9 MiB
Average record size in memory100.0 B

Variable types

Numeric7
Categorical44

Alerts

Dataset has 1528 (3.7%) duplicate rowsDuplicates
pdays is highly correlated with previous and 1 other fieldsHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
cons.price.idx is highly correlated with contact_telephoneHigh correlation
cons.conf.idx is highly correlated with month_augHigh correlation
marital_married is highly correlated with marital_singleHigh correlation
marital_single is highly correlated with marital_marriedHigh correlation
housing_unknown is highly correlated with loan_unknownHigh correlation
loan_unknown is highly correlated with housing_unknownHigh correlation
contact_telephone is highly correlated with cons.price.idxHigh correlation
month_aug is highly correlated with cons.conf.idxHigh correlation
poutcome_nonexistent is highly correlated with previousHigh correlation
poutcome_success is highly correlated with pdaysHigh correlation
pdays is highly correlated with previous and 1 other fieldsHigh correlation
previous is highly correlated with pdays and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with euribor3m and 1 other fieldsHigh correlation
euribor3m is highly correlated with cons.price.idxHigh correlation
marital_married is highly correlated with marital_singleHigh correlation
marital_single is highly correlated with marital_marriedHigh correlation
housing_unknown is highly correlated with loan_unknownHigh correlation
loan_unknown is highly correlated with housing_unknownHigh correlation
contact_telephone is highly correlated with cons.price.idxHigh correlation
poutcome_nonexistent is highly correlated with previousHigh correlation
poutcome_success is highly correlated with pdays and 1 other fieldsHigh correlation
pdays is highly correlated with previous and 1 other fieldsHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
cons.price.idx is highly correlated with contact_telephoneHigh correlation
marital_married is highly correlated with marital_singleHigh correlation
marital_single is highly correlated with marital_marriedHigh correlation
housing_unknown is highly correlated with loan_unknownHigh correlation
loan_unknown is highly correlated with housing_unknownHigh correlation
contact_telephone is highly correlated with cons.price.idxHigh correlation
poutcome_nonexistent is highly correlated with previousHigh correlation
poutcome_success is highly correlated with pdaysHigh correlation
marital_single is highly correlated with marital_marriedHigh correlation
housing_unknown is highly correlated with loan_unknownHigh correlation
marital_married is highly correlated with marital_singleHigh correlation
loan_unknown is highly correlated with housing_unknownHigh correlation
age is highly correlated with job_retired and 1 other fieldsHigh correlation
pdays is highly correlated with previous and 4 other fieldsHigh correlation
previous is highly correlated with pdays and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with cons.conf.idx and 8 other fieldsHigh correlation
cons.conf.idx is highly correlated with pdays and 14 other fieldsHigh correlation
euribor3m is highly correlated with pdays and 10 other fieldsHigh correlation
job_blue-collar is highly correlated with education_basic.9y and 1 other fieldsHigh correlation
job_retired is highly correlated with ageHigh correlation
job_services is highly correlated with education_high.schoolHigh correlation
job_technician is highly correlated with education_professional.courseHigh correlation
marital_married is highly correlated with marital_singleHigh correlation
marital_single is highly correlated with age and 1 other fieldsHigh correlation
education_basic.9y is highly correlated with job_blue-collarHigh correlation
education_high.school is highly correlated with job_services and 1 other fieldsHigh correlation
education_professional.course is highly correlated with job_technicianHigh correlation
education_university.degree is highly correlated with job_blue-collar and 1 other fieldsHigh correlation
housing_unknown is highly correlated with loan_unknownHigh correlation
loan_unknown is highly correlated with housing_unknownHigh correlation
contact_telephone is highly correlated with cons.price.idx and 4 other fieldsHigh correlation
month_aug is highly correlated with cons.price.idx and 1 other fieldsHigh correlation
month_dec is highly correlated with cons.conf.idxHigh correlation
month_jul is highly correlated with cons.price.idx and 1 other fieldsHigh correlation
month_jun is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
month_mar is highly correlated with cons.conf.idx and 1 other fieldsHigh correlation
month_may is highly correlated with cons.price.idx and 3 other fieldsHigh correlation
month_nov is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
month_oct is highly correlated with cons.price.idx and 1 other fieldsHigh correlation
month_sep is highly correlated with cons.conf.idxHigh correlation
poutcome_nonexistent is highly correlated with pdays and 4 other fieldsHigh correlation
poutcome_success is highly correlated with pdays and 3 other fieldsHigh correlation
y is highly correlated with cons.conf.idx and 1 other fieldsHigh correlation
previous has 35563 (86.3%) zeros Zeros

Reproduction

Analysis started2022-01-27 14:11:24.361924
Analysis finished2022-01-27 14:12:09.270750
Duration44.91 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02406041
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:09.433314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42124998
Coefficient of variation (CV)0.2603746315
Kurtosis0.7913115312
Mean40.02406041
Median Absolute Deviation (MAD)7
Skewness0.7846968158
Sum1648511
Variance108.6024512
MonotonicityNot monotonic
2022-01-27T15:12:09.610840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311947
 
4.7%
321846
 
4.5%
331833
 
4.5%
361780
 
4.3%
351759
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391432
 
3.5%
Other values (68)24204
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24463
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567592503
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:09.790320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770013543
Coefficient of variation (CV)1.078836903
Kurtosis36.97979514
Mean2.567592503
Median Absolute Deviation (MAD)1
Skewness4.762506697
Sum105754
Variance7.672975028
MonotonicityNot monotonic
2022-01-27T15:12:09.934973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.475454
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:10.084534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9109073
Coefficient of variation (CV)0.194198103
Kurtosis22.22946263
Mean962.475454
Median Absolute Deviation (MAD)0
Skewness-4.922189916
Sum39642439
Variance34935.68728
MonotonicityNot monotonic
2022-01-27T15:12:10.227190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99939673
96.3%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (17)205
 
0.5%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
546
 
0.1%
6412
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
99939673
96.3%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
 
< 0.1%
178
 
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729629989
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:10.367808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949010798
Coefficient of variation (CV)2.861311858
Kurtosis20.10881622
Mean0.1729629989
Median Absolute Deviation (MAD)0
Skewness3.832042243
Sum7124
Variance0.2449270788
MonotonicityNot monotonic
2022-01-27T15:12:10.511393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035563
86.3%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57566437
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:10.698931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.578840049
Coefficient of variation (CV)0.00618579684
Kurtosis-0.8298085772
Mean93.57566437
Median Absolute Deviation (MAD)0.38
Skewness-0.2308876514
Sum3854194.464
Variance0.3350558023
MonotonicityNot monotonic
2022-01-27T15:12:10.869473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947763
18.8%
93.9186685
16.2%
92.8935794
14.1%
93.4445175
12.6%
94.4654374
10.6%
93.23616
8.8%
93.0752458
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431447
 
1.1%
92.469178
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935794
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947763
18.8%
93.9186685
16.2%
93.876212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50260027
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41188
Negative (%)100.0%
Memory size321.9 KiB
2022-01-27T15:12:11.022027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.628197856
Coefficient of variation (CV)-0.1142691537
Kurtosis-0.3585583105
Mean-40.50260027
Median Absolute Deviation (MAD)4.4
Skewness0.3031798587
Sum-1668221.1
Variance21.4202154
MonotonicityNot monotonic
2022-01-27T15:12:11.312289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47763
18.8%
-42.76685
16.2%
-46.25794
14.1%
-36.15175
12.6%
-41.84374
10.6%
-423616
8.8%
-47.12458
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12458
 
6.0%
-46.25794
14.1%
-45.910
 
< 0.1%
-42.76685
16.2%
-423616
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9447
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6178
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15175
12.6%
-36.47763
18.8%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621290813
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-01-27T15:12:11.475812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734447405
Coefficient of variation (CV)0.4789583313
Kurtosis-1.406802622
Mean3.621290813
Median Absolute Deviation (MAD)0.108
Skewness-0.7091879564
Sum149153.726
Variance3.0083078
MonotonicityNot monotonic
2022-01-27T15:12:11.636428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622613
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651071
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24636
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968992
 
2.4%
4.967643
 
1.6%
4.966622
 
1.5%
4.9651071
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622613
6.3%

job_blue-collar
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
31934 
1
9254 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031934
77.5%
19254
 
22.5%

Length

2022-01-27T15:12:11.813949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:11.894691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
031934
77.5%
19254
 
22.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_entrepreneur
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
39732 
1
 
1456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039732
96.5%
11456
 
3.5%

Length

2022-01-27T15:12:12.571004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:12.664753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039732
96.5%
11456
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_housemaid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40128 
1
 
1060

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040128
97.4%
11060
 
2.6%

Length

2022-01-27T15:12:12.780404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:12.881136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040128
97.4%
11060
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_management
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
38264 
1
 
2924

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
038264
92.9%
12924
 
7.1%

Length

2022-01-27T15:12:12.982906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:13.077649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
038264
92.9%
12924
 
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_retired
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
39468 
1
 
1720

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039468
95.8%
11720
 
4.2%

Length

2022-01-27T15:12:13.179377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:13.272134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039468
95.8%
11720
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
39767 
1
 
1421

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039767
96.5%
11421
 
3.5%

Length

2022-01-27T15:12:13.375815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:13.468603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039767
96.5%
11421
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_services
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
37219 
1
3969 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
037219
90.4%
13969
 
9.6%

Length

2022-01-27T15:12:13.574308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:13.668074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
037219
90.4%
13969
 
9.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_student
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40313 
1
 
875

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040313
97.9%
1875
 
2.1%

Length

2022-01-27T15:12:13.775742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:13.876517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040313
97.9%
1875
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_technician
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
34445 
1
6743 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034445
83.6%
16743
 
16.4%

Length

2022-01-27T15:12:13.985183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:14.081962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
034445
83.6%
16743
 
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_unemployed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40174 
1
 
1014

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040174
97.5%
11014
 
2.5%

Length

2022-01-27T15:12:14.184647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:14.280435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040174
97.5%
11014
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

job_unknown
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40858 
1
 
330

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040858
99.2%
1330
 
0.8%

Length

2022-01-27T15:12:14.385111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:14.477904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040858
99.2%
1330
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marital_married
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
1
24928 
0
16260 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
124928
60.5%
016260
39.5%

Length

2022-01-27T15:12:14.580590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:14.674341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
124928
60.5%
016260
39.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marital_single
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
29620 
1
11568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029620
71.9%
111568
 
28.1%

Length

2022-01-27T15:12:14.778104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:14.868861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
029620
71.9%
111568
 
28.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marital_unknown
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
41108 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041108
99.8%
180
 
0.2%

Length

2022-01-27T15:12:14.970585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:15.071316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
041108
99.8%
180
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
38896 
1
 
2292

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
038896
94.4%
12292
 
5.6%

Length

2022-01-27T15:12:15.173003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:15.424333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
038896
94.4%
12292
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

education_basic.9y
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
35143 
1
6045 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035143
85.3%
16045
 
14.7%

Length

2022-01-27T15:12:15.530089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:15.626791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
035143
85.3%
16045
 
14.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

education_high.school
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
31673 
1
9515 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
031673
76.9%
19515
 
23.1%

Length

2022-01-27T15:12:15.729515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:15.822311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
031673
76.9%
19515
 
23.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
41170 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041170
> 99.9%
118
 
< 0.1%

Length

2022-01-27T15:12:15.929979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:16.031706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
041170
> 99.9%
118
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

education_professional.course
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
35945 
1
5243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035945
87.3%
15243
 
12.7%

Length

2022-01-27T15:12:16.144446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:16.252183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
035945
87.3%
15243
 
12.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

education_university.degree
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
29020 
1
12168 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029020
70.5%
112168
29.5%

Length

2022-01-27T15:12:16.366811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:16.495584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
029020
70.5%
112168
29.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
39457 
1
 
1731

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039457
95.8%
11731
 
4.2%

Length

2022-01-27T15:12:16.627231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:16.740929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039457
95.8%
11731
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

default_unknown
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
32591 
1
8597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
032591
79.1%
18597
 
20.9%

Length

2022-01-27T15:12:16.856618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:16.952394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
032591
79.1%
18597
 
20.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

default_yes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
41185 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041185
> 99.9%
13
 
< 0.1%

Length

2022-01-27T15:12:17.059115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:17.158849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
041185
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

housing_unknown
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40198 
1
 
990

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040198
97.6%
1990
 
2.4%

Length

2022-01-27T15:12:17.299470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:17.420150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040198
97.6%
1990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

housing_yes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
1
21576 
0
19612 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
121576
52.4%
019612
47.6%

Length

2022-01-27T15:12:17.529815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:17.626597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
121576
52.4%
019612
47.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loan_unknown
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40198 
1
 
990

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040198
97.6%
1990
 
2.4%

Length

2022-01-27T15:12:17.728330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:17.821082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040198
97.6%
1990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loan_yes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
34940 
1
6248 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
034940
84.8%
16248
 
15.2%

Length

2022-01-27T15:12:17.924762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:18.018553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
034940
84.8%
16248
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

contact_telephone
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
26144 
1
15044 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
026144
63.5%
115044
36.5%

Length

2022-01-27T15:12:18.124648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:18.217444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
026144
63.5%
115044
36.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_aug
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
35010 
1
6178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035010
85.0%
16178
 
15.0%

Length

2022-01-27T15:12:18.324115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:18.450839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
035010
85.0%
16178
 
15.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_dec
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
41006 
1
 
182

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041006
99.6%
1182
 
0.4%

Length

2022-01-27T15:12:18.585515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:18.693187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
041006
99.6%
1182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_jul
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
34014 
1
7174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034014
82.6%
17174
 
17.4%

Length

2022-01-27T15:12:18.804936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:18.904665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
034014
82.6%
17174
 
17.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_jun
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
35870 
1
5318 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035870
87.1%
15318
 
12.9%

Length

2022-01-27T15:12:19.017361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:19.118094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
035870
87.1%
15318
 
12.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_mar
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40642 
1
 
546

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040642
98.7%
1546
 
1.3%

Length

2022-01-27T15:12:19.374408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:19.467159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040642
98.7%
1546
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_may
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
27419 
1
13769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
027419
66.6%
113769
33.4%

Length

2022-01-27T15:12:19.568888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:19.661641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
027419
66.6%
113769
33.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_nov
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
37087 
1
4101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037087
90.0%
14101
 
10.0%

Length

2022-01-27T15:12:19.763364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:19.867091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
037087
90.0%
14101
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_oct
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40470 
1
 
718

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040470
98.3%
1718
 
1.7%

Length

2022-01-27T15:12:19.981419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:20.094116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040470
98.3%
1718
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_sep
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
40618 
1
 
570

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040618
98.6%
1570
 
1.4%

Length

2022-01-27T15:12:20.211801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:20.309540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
040618
98.6%
1570
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day_of_week_mon
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
32674 
1
8514 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
032674
79.3%
18514
 
20.7%

Length

2022-01-27T15:12:20.430216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:20.541917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
032674
79.3%
18514
 
20.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day_of_week_thu
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
32565 
1
8623 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
032565
79.1%
18623
 
20.9%

Length

2022-01-27T15:12:20.654616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:20.756359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
032565
79.1%
18623
 
20.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day_of_week_tue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
33098 
1
8090 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033098
80.4%
18090
 
19.6%

Length

2022-01-27T15:12:20.866050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:20.962831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
033098
80.4%
18090
 
19.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day_of_week_wed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
33054 
1
8134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033054
80.3%
18134
 
19.7%

Length

2022-01-27T15:12:21.065516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:21.159266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
033054
80.3%
18134
 
19.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

poutcome_nonexistent
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
1
35563 
0
5625 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
135563
86.3%
05625
 
13.7%

Length

2022-01-27T15:12:21.263029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:21.356785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
135563
86.3%
05625
 
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

poutcome_success
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
39815 
1
 
1373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039815
96.7%
11373
 
3.3%

Length

2022-01-27T15:12:21.458506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:21.550264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039815
96.7%
11373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

y
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
0
36548 
1
4640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036548
88.7%
14640
 
11.3%

Length

2022-01-27T15:12:21.651993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T15:12:21.747737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
036548
88.7%
14640
 
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-27T15:12:05.655457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:58.268103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:59.719087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.041124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.167778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.400966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.531464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:05.828994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:58.574287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:59.900603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.222637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.355237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.579489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.708934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:06.132182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:58.781764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:00.066106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.382199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.519798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.743051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.868549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:06.285716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:58.970974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:00.241209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.539774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.689384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.898689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:05.023150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:06.448324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:59.177371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:00.569384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.710320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.891843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.064190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:05.196671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:06.600914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:59.352957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:00.727946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:01.864607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.061873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.218832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:05.350220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:06.751526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:11:59.545538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:00.887535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:02.018200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:03.223495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:04.376876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-27T15:12:05.503848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-01-27T15:12:21.933683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-27T15:12:24.174647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-27T15:12:26.475913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-27T15:12:28.949302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-27T15:12:29.702287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-27T15:12:07.366366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agecampaignpdayspreviouscons.price.idxcons.conf.idxeuribor3mjob_blue-collarjob_entrepreneurjob_housemaidjob_managementjob_retiredjob_self-employedjob_servicesjob_studentjob_technicianjob_unemployedjob_unknownmarital_marriedmarital_singlemarital_unknowneducation_basic.6yeducation_basic.9yeducation_high.schooleducation_illiterateeducation_professional.courseeducation_university.degreeeducation_unknowndefault_unknowndefault_yeshousing_unknownhousing_yesloan_unknownloan_yescontact_telephonemonth_augmonth_decmonth_julmonth_junmonth_marmonth_maymonth_novmonth_octmonth_sepday_of_week_monday_of_week_thuday_of_week_tueday_of_week_wedpoutcome_nonexistentpoutcome_successy
0561999093.994-36.44.85700100000000100000000000000010000010001000100
1571999093.994-36.44.85700000010000100001000010000010000010001000100
2371999093.994-36.44.85700000010000100001000000010010000010001000100
3401999093.994-36.44.85700000000000100100000000000010000010001000100
4561999093.994-36.44.85700000010000100001000000000110000010001000100
5451999093.994-36.44.85700000010000100010000010000010000010001000100
6591999093.994-36.44.85700000000000100000010000000010000010001000100
7411999093.994-36.44.85710000000000100000000110000010000010001000100
8241999093.994-36.44.85700000000100010000010000010010000010001000100
9251999093.994-36.44.85700000010000010001000000010010000010001000100

Last rows

agecampaignpdayspreviouscons.price.idxcons.conf.idxeuribor3mjob_blue-collarjob_entrepreneurjob_housemaidjob_managementjob_retiredjob_self-employedjob_servicesjob_studentjob_technicianjob_unemployedjob_unknownmarital_marriedmarital_singlemarital_unknowneducation_basic.6yeducation_basic.9yeducation_high.schooleducation_illiterateeducation_professional.courseeducation_university.degreeeducation_unknowndefault_unknowndefault_yeshousing_unknownhousing_yesloan_unknownloan_yescontact_telephonemonth_augmonth_decmonth_julmonth_junmonth_marmonth_maymonth_novmonth_octmonth_sepday_of_week_monday_of_week_thuday_of_week_tueday_of_week_wedpoutcome_nonexistentpoutcome_successy
411786226394.767-50.81.03100001000000100000001000000000000001000100011
41179643999094.767-50.81.02800001000000000000010000010000000001000000100
41180362999094.767-50.81.02800000000000100000001000000000000001000000100
41181371999094.767-50.81.02800000000000100000001000010000000001000000101
411822919194.767-50.81.02800000000010010000000000010000000001000000010
41183731999094.767-50.81.02800001000000100000010000010000000001000000101
41184461999094.767-50.81.02810000000000100000010000000000000001000000100
41185562999094.767-50.81.02800001000000100000001000010000000001000000100
41186441999094.767-50.81.02800000000100100000010000000000000001000000101
41187743999194.767-50.81.02800001000000100000010000010000000001000000000

Duplicate rows

Most frequently occurring

agecampaignpdayspreviouscons.price.idxcons.conf.idxeuribor3mjob_blue-collarjob_entrepreneurjob_housemaidjob_managementjob_retiredjob_self-employedjob_servicesjob_studentjob_technicianjob_unemployedjob_unknownmarital_marriedmarital_singlemarital_unknowneducation_basic.6yeducation_basic.9yeducation_high.schooleducation_illiterateeducation_professional.courseeducation_university.degreeeducation_unknowndefault_unknowndefault_yeshousing_unknownhousing_yesloan_unknownloan_yescontact_telephonemonth_augmonth_decmonth_julmonth_junmonth_marmonth_maymonth_novmonth_octmonth_sepday_of_week_monday_of_week_thuday_of_week_tueday_of_week_wedpoutcome_nonexistentpoutcome_successy# duplicates
92271999093.918-42.74.962000000000000100010000000000000100000000101007
128281999093.918-42.74.962000000100001000010000000000000100000000101007
129281999093.918-42.74.962000000100001000010000000100000100000000101006
1109431999093.918-42.74.962001000000001000000000000100000100000000011006
24241999093.918-42.74.962000000100000100000100000100000100000000011005
88271999093.918-42.74.961000000000000100000010100100000100000000101005
250301999093.444-36.14.964000000000000100010000000000010000000000001005
317311999093.200-42.04.076000000000000100000010000100000000010001001005
318311999093.200-42.04.076000000000001000000010000000000000010001001005
420321999093.444-36.14.964000000000000100000010000100010000000001001005